Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)

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dc.authorid0000-0002-3452-1889en_US
dc.authorid0000-0003-4948-6905en_US
dc.authorid0000-0002-3586-2570en_US
dc.authorid0000-0002-7567-9885en_US
dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorBekun, Festus Victor
dc.contributor.authorAdedoyin, Festus Fatai
dc.contributor.authorGyamfi, Bright Akwasi
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.date.accessioned2023-11-08T13:31:26Z
dc.date.available2023-11-08T13:31:26Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThis study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE re search published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organi zations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents compris ing 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.en_US
dc.identifier.doi10.3390/cleantechnol5020026en_US
dc.identifier.endpage517en_US
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85163772967en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage497en_US
dc.identifier.urihttps://hdl.handle.net/11467/6960
dc.identifier.urihttps://doi.org/10.3390/cleantechnol5020026
dc.identifier.volume5en_US
dc.identifier.wosWOS:001014157600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofClean Technologiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmachine learning; algorithms; supervised learning; unsupervised learning; deep learning; renewable energy; forecasting; optimizationen_US
dc.titleMachine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)en_US
dc.typeArticleen_US

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